Efficient forecasting model technique for river stream flow in tropical environment
Monthly stream flow forecasting can provide crucial information on hydrological applications including water resource management and flood mitigation systems. In this statistical study, time series and artificial intelligence methods were evaluated according to implementation of each time-series tec...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor and Francis
2019
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| Online Access: | http://psasir.upm.edu.my/id/eprint/82681/ http://psasir.upm.edu.my/id/eprint/82681/1/Efficient%20forecasting%20model%20technique.pdf |
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| author | Khairuddin, Nuruljannah Aris, Ahmad Zaharin Elshafie, Ahmed Narany, Tahoora Sheikhy Ishak, Mohd Yusoff Mohd Isa, Noorain |
| author_facet | Khairuddin, Nuruljannah Aris, Ahmad Zaharin Elshafie, Ahmed Narany, Tahoora Sheikhy Ishak, Mohd Yusoff Mohd Isa, Noorain |
| author_sort | Khairuddin, Nuruljannah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Monthly stream flow forecasting can provide crucial information on hydrological applications including water resource management and flood mitigation systems. In this statistical study, time series and artificial intelligence methods were evaluated according to implementation of each time-series technique to find an effective tool for stream flow prediction in flood forecasting. This paper explores the application of water level, rainfall data and input time series into three different models; linear regression (LR), auto-regressive integrated moving average (ARIMA) and artificial neural networks (ANN). The performances of the models were compared based on the maximum coefficient of determination (R2) and minimum root means square error (RMSE). Based on the results the ANN model presents the most accurate measurement, with the R2 value of 0.868 and 18% RMSE. The present study suggests that ANN is the best model due to its ability to recognise times series patterns and to understand non-linear relationships. |
| first_indexed | 2025-11-15T12:27:35Z |
| format | Article |
| id | upm-82681 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:27:35Z |
| publishDate | 2019 |
| publisher | Taylor and Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-826812021-12-06T01:57:06Z http://psasir.upm.edu.my/id/eprint/82681/ Efficient forecasting model technique for river stream flow in tropical environment Khairuddin, Nuruljannah Aris, Ahmad Zaharin Elshafie, Ahmed Narany, Tahoora Sheikhy Ishak, Mohd Yusoff Mohd Isa, Noorain Monthly stream flow forecasting can provide crucial information on hydrological applications including water resource management and flood mitigation systems. In this statistical study, time series and artificial intelligence methods were evaluated according to implementation of each time-series technique to find an effective tool for stream flow prediction in flood forecasting. This paper explores the application of water level, rainfall data and input time series into three different models; linear regression (LR), auto-regressive integrated moving average (ARIMA) and artificial neural networks (ANN). The performances of the models were compared based on the maximum coefficient of determination (R2) and minimum root means square error (RMSE). Based on the results the ANN model presents the most accurate measurement, with the R2 value of 0.868 and 18% RMSE. The present study suggests that ANN is the best model due to its ability to recognise times series patterns and to understand non-linear relationships. Taylor and Francis 2019-06-17 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/82681/1/Efficient%20forecasting%20model%20technique.pdf Khairuddin, Nuruljannah and Aris, Ahmad Zaharin and Elshafie, Ahmed and Narany, Tahoora Sheikhy and Ishak, Mohd Yusoff and Mohd Isa, Noorain (2019) Efficient forecasting model technique for river stream flow in tropical environment. Urban Water Journal, 16 (3). pp. 183-192. ISSN 1573-062X; ESSN: 1744-9006 https://www.tandfonline.com/doi/full/10.1080/1573062X.2019.1637906 10.1080/1573062X.2019.1637906 |
| spellingShingle | Khairuddin, Nuruljannah Aris, Ahmad Zaharin Elshafie, Ahmed Narany, Tahoora Sheikhy Ishak, Mohd Yusoff Mohd Isa, Noorain Efficient forecasting model technique for river stream flow in tropical environment |
| title | Efficient forecasting model technique for river stream flow in tropical environment |
| title_full | Efficient forecasting model technique for river stream flow in tropical environment |
| title_fullStr | Efficient forecasting model technique for river stream flow in tropical environment |
| title_full_unstemmed | Efficient forecasting model technique for river stream flow in tropical environment |
| title_short | Efficient forecasting model technique for river stream flow in tropical environment |
| title_sort | efficient forecasting model technique for river stream flow in tropical environment |
| url | http://psasir.upm.edu.my/id/eprint/82681/ http://psasir.upm.edu.my/id/eprint/82681/ http://psasir.upm.edu.my/id/eprint/82681/ http://psasir.upm.edu.my/id/eprint/82681/1/Efficient%20forecasting%20model%20technique.pdf |